npj Computational Materials最新文献_第4页

Virtual melting and cyclic transformations between amorphous Si, Si I, and Si IV in a shear band at room temperature

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-03 DOI: 10.1038/s41524-025-01537-1

Hao Chen, Valery I. Levitas

{"title":"Virtual melting and cyclic transformations between amorphous Si, Si I, and Si IV in a shear band at room temperature","authors":"Hao Chen, Valery I. Levitas","doi":"10.1038/s41524-025-01537-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01537-1","url":null,"abstract":"Virtual melting (VM) as alternative deformation and stress relaxation mechanisms under extreme load is directly validated by molecular dynamics (MD) simulations of the simple shear of single crystal Si I at a temperature 1383 K below the melting temperature. The shear band consisting of liquid Si is formed immediately after the shear instability while stresses drop to zero. This process is independent of the applied shear rate. A new thermodynamic approach is developed, and the thermodynamic criterion for VM, which depends on the ratio of the sample to shear band widths, is derived analytically and confirmed by MD simulations. Since stress-free melt is unstable at 300 K, with further shear, the VM immediately transforms to a mixture of low-density amorphous a-Si, stable Si I, and metastable Si IV. Cyclic transformations between a-Si ↔ Si I, a-Si ↔ Si IV, and Si I ↔ Si IV with volume fraction of all phases mostly between 0.2 and 0.4 and non-repeatable nanostructure evolution are reveled. Such cyclic transformations produce additional important carriers for plastic deformation through transformation strain and transformation-induced plasticity due to volume change, which may occur in shear bands in various material systems but missed in experiments and simulations. The release of shear stresses quenches the microstructure, and shows reasonable qualitative correspondence with existing experiments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MGNN: Moment Graph Neural Network for Universal Molecular Potentials MGNN：通用分子势矩图神经网络

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01541-5

Jian Chang, Shuze Zhu

{"title":"MGNN: Moment Graph Neural Network for Universal Molecular Potentials","authors":"Jian Chang, Shuze Zhu","doi":"10.1038/s41524-025-01541-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01541-5","url":null,"abstract":"The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has marked a transformative era in graph-based learning, particularly in the realm of predicting chemical properties and expediting molecular dynamics studies. We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. From benchmark tests on public datasets, MGNN delivers multiple state-of-the-art results on QM9, revised MD17 and MD17-ethanol. Its generalizability and efficiency are also tested in additional systems including 3BPA and 25-element high-entropy alloys. The prowess of MGNN also extends to dynamic simulations, accurately predicting the structural and kinetic properties of complex systems such as amorphous electrolytes, with results that closely align with those from ab-initio simulations. The application of MGNN to the simulation of molecular spectra exemplifies its potential to offer a promising alternative to traditional electronic structure methods.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings 利用多步非线性自回归和图嵌入进行快速高保真量子模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-024-01479-0

Akeel A. Shah, P. K. Leung, W. W. Xing

{"title":"Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings","authors":"Akeel A. Shah, P. K. Leung, W. W. Xing","doi":"10.1038/s41524-024-01479-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01479-0","url":null,"abstract":"The design and high-throughput screening of materials using machine-learning assisted quantum-mechanical simulations typically requires the existence of a very large data set, often generated from simulations at a high level of theory or fidelity. A single simulation at high fidelity can take on the order of days for a complex molecule. Thus, although machine learning surrogate simulations seem promising at first glance, generation of the training data can defeat the original purpose. For this reason, the use of machine learning to screen or design materials remains elusive for many important applications. In this paper we introduce a new multi-fidelity approach based on a dual graph embedding to extract features that are placed inside a nonlinear multi-step autoregressive model. Experiments on five benchmark problems, with 14 different quantities and 27 different levels of theory, demonstrate the generalizability and high accuracy of the approach. It typically requires a few 10s to a few 1000’s of high-fidelity training points, which is several orders of magnitude lower than direct ML methods, and can be up to two orders of magnitude lower than other multi-fidelity methods. Furthermore, we develop a new benchmark data set for 860 benzoquinone molecules with up to 14 atoms, containing energy, HOMO, LUMO and dipole moment values at four levels of theory, up to coupled cluster with singles and doubles.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of 2D skyrmionic metamaterials through controlled assembly 通过受控装配设计二维天磁超材料

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01534-4

Qichen Xu, Zhuanglin Shen, Alexander Edström, I. P. Miranda, Zhiwei Lu, Anders Bergman, Danny Thonig, Wanjian Yin, Olle Eriksson, Anna Delin

{"title":"Design of 2D skyrmionic metamaterials through controlled assembly","authors":"Qichen Xu, Zhuanglin Shen, Alexander Edström, I. P. Miranda, Zhiwei Lu, Anders Bergman, Danny Thonig, Wanjian Yin, Olle Eriksson, Anna Delin","doi":"10.1038/s41524-025-01534-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01534-4","url":null,"abstract":"Despite extensive research on magnetic skyrmions and antiskyrmions, a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying, or even tailor-made, topologies. We address this challenge, by focusing on a construction pathway of skyrmionic metamaterials within a monolayer thin film and suggest several skyrmionic metamaterials that are surprisingly stable, i.e., long-lived, due to a self-stabilization mechanism. This makes these new textures promising for applications. Central to our approach is the concept of ’simulated controlled assembly’, in short, a protocol inspired by ’click chemistry’ that allows for positioning topological magnetic structures where one likes, and then allowing for energy minimization to elucidate the stability. Utilizing high-throughput atomistic-spin-dynamic simulations alongside state-of-the-art AI-driven tools, we have isolated skyrmions (topological charge Q = 1), antiskyrmions (Q = − 1), and skyrmionium (Q = 0). These entities serve as foundational ’skyrmionic building blocks’ to form the here-reported intricate textures. In this work, two key contributions are introduced to the field of skyrmionic systems. First, we present a novel combination of atomistic spin dynamics simulations and controlled assembly protocols for the stabilization and investigation of new topological magnets. Second, using the aforementioned methods we report on the discovery of skyrmionic metamaterials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"189 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations 通过机器学习驱动的原子模拟了解动态压缩条件下的α-石英相变

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01542-4

Linus C. Erhard, Christoph Otzen, Jochen Rohrer, Clemens Prescher, Karsten Albe

引用次数: 0

Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01539-z

Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou

{"title":"Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI","authors":"Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou","doi":"10.1038/s41524-025-01539-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01539-z","url":null,"abstract":"Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01548-y

Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni

{"title":"EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys","authors":"Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni","doi":"10.1038/s41524-025-01548-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01548-y","url":null,"abstract":"Using machine learning to predict and design materials is an important mean of accelerating material development. One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors. However, the complexity of computing material structures limits the practical use of these models. To address this challenge and improve prediction accuracy in small data sets, we develop a generative network framework: Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks (EFTGAN). Combining the elemental convolution technique with Generative Adversarial Networks (GAN), EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy, but also for prediction when the structures are unknown. Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys, we successfully improve the prediction accuracy in a small data set and predict the concentration-dependent formation energies, lattices, and magnetic moments in quinary systems. This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs, which is effective and accurate for the prediction and development of materials for small data sets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stacking-dependent electronic and topological properties in van der Waals antiferromagnet MnBi2Te4 films

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01545-1

Jiaheng Li, Quansheng Wu, Hongming Weng

{"title":"Stacking-dependent electronic and topological properties in van der Waals antiferromagnet MnBi2Te4 films","authors":"Jiaheng Li, Quansheng Wu, Hongming Weng","doi":"10.1038/s41524-025-01545-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01545-1","url":null,"abstract":"Combining first-principles calculations and tight-binding Hamiltonians, we study the stack-dependent behaviour of electronic and topological properties of layered antiferromagnet MnBi2Te4. Lateral shift of top septuple-layer greatly modifies electronic properties, and even induces topological phase transition between quantum anomalous Hall (QAH) insulators with C = 1 and trivial magnetic insulators with C = 0. The local energy minimum of “incorrect\" stacking order exhibits thickness-dependent topology opposite to the usual stacking order, which is attribute to relatively weakened interlayer Te-Te interaction in “incorrect\" stacking configuration. Our effective model analysis provides a comprehensive understanding of the underlying mechanisms involved, and we also propose two optical setups that can effectively differentiate between different stacking configurations. Our findings underscores the nuanced and profound influence that interlayer sliding in magnetic topological materials can have on the macroscopic quantum states, opening new avenues for the design and engineering of topological quantum materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Construction of a knowledge graph for framework material enabled by large language models and its application

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-27 DOI: 10.1038/s41524-025-01540-6

Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, Jian-Rong Li

{"title":"Construction of a knowledge graph for framework material enabled by large language models and its application","authors":"Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, Jian-Rong Li","doi":"10.1038/s41524-025-01540-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01540-6","url":null,"abstract":"Framework materials (FMs) have been extensively investigated with a plethora of literature documenting their unique properties and potential applications. Despite this, a comprehensive knowledge graph for this emerging field has not yet been constructed. In this study, by utilizing the natural language processing capabilities of large language models (LLMs), we have established a comprehensive knowledge graph (KG-FM). It covers synthesis, properties, applications, and other aspects of FMs including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs). The knowledge graph was constructed through the analysis of over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships. Subsequently, its application has been explored for enhancing data retrieval, mining, and the development of sophisticated question-answering systems. Especially when integrating the KGs with LLMs, resulted Qwen2-KG not only achieves a higher accuracy rate of 91.67% in question-answering than existing models but also provides precise information sources.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised identification of crystal defects from atomistic potential descriptors

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-27 DOI: 10.1038/s41524-025-01544-2

Lukáš Kývala, Pablo Montero de Hijes, Christoph Dellago

{"title":"Unsupervised identification of crystal defects from atomistic potential descriptors","authors":"Lukáš Kývala, Pablo Montero de Hijes, Christoph Dellago","doi":"10.1038/s41524-025-01544-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01544-2","url":null,"abstract":"Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0